Multi indicators Multi objective Evolutionary Algorithm with Q Learning for Real world Network Optimization

To help design a cost-effective RFID-based HMSC network, first, a deterministic four-objective mixed integer linear programming model was developed and used for investigating

Applying Linear Regression and Neural Network Meta- Models for Evolutionary Algorithm Based Simulation Optimization.. (Under the direction of Dr.

The aim of this research is to improve the weight and structure of the TBP network simultaneously, and to achieve a better optimized network performance,

This paper studies the modeling and optimization of energy use and greenhouse gas emissions of eggplant production using artificial neural network and multi-objective genetic

The comparison of number of alive nodes in each round algorithms, the deployment solution can be obtained by the Hybrid-MOEA/D-II algorithm can make the network in each round

In this work, we propose a learning-guided multi-objective evolutionary al- gorithm (MODEwAwL) for the constrained portfolio optimization. The pro- posed algorithm adopts a new

Backpropagation Neural Network Based on Local Search Strategy and Enhanced Multi-objective Evolutionary Algorithm for Breast

This paper proposed an algorithmic framework to solve the RFID reader placement problem as multi-objective optimization problem using Differential Evolution algorithm